Abstract:
Abstract
Introduction: Mitochondrial disorders exhibit unparalleled clinical and genetic
complexity due to dual-genomic control, typically manifesting in pediatric populations
with an ultra-rapid, multi-systemic progression that challenges conventional diagnostic
boundaries. This study evaluates the phenotypic and molecular spectrum of 39
predominantly pediatric patients with mitochondrial involvement, identified from a
broader multi-systemic referral cohort of 240 children.
Materials and Methods: Primary enrollment comprised patients achieving a
modified Nijmegen Mitochondrial Disease Score (NMDS) ≥ 3. The dual-genomic
workflow paired peripheral blood quantitative PCR-high resolution melting screening
with targeted mitochondrial DNA sequencing, real-time qPCR for quantitative copy
number evaluation, and high-throughput nuclear genomic analysis. Clinical, metabolic,
instrumental and neuroimaging parameters were cross-linked using a Spearman
correlation matrix.
Results: The cohort exhibited a highly intricate neurodevelopmental and systemic
phenotype, prominently dominated by pervasive multi-organ involvement (87.2%),
seizures (69.2%), brainstem involvement (43.6%), and marked neuromuscular
dysfunction (41.0%). Biochemical profiling confirmed systemic metabolic disruption,
primarily presenting as hyperlactatemia (89.7%) and hyperalaninemia (38.5%).
Instrumental and neuroimaging assessments revealed abnormal neuroradiological
(74.4%), electroencephalographic (53.8%), and electrocardiographic (30.8%) profiles.
Spearman correlation analysis demonstrated that target group status was strongly
anchored by hyperlactatemia (ρ = 0.40) and hyperalaninemia (ρ = 0.38), clustering
tightly (ρ ≈ 0.26) with basal ganglia anomalies, neuromuscular dysfunction, and
developmental regression.
Conclusions: This study demonstrates that an integrated, multi-tiered genomic
workflow successfully identifies relevant genetic variants across both the mitochondrial
and nuclear genomes in 16.3% of cases within a complex screening pool. Pairing a
modified clinical scoring system and core metabolic biomarkers with key neuroimaging
findings establishes a dependable framework for identifying high-probability cases.
